Object Detection under Varying Illumination Based on Adaptive Background Modeling Considering Spatial Locality

  • Tatsuya Tanaka
  • Atsushi Shimada
  • Daisaku Arita
  • Rin-ichiro Taniguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

We propose a new method for background modeling. Our method is based on the two complementary approaches. One uses the probability density function(PDF) to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. And foreground object is detected based on the estimated PDF. The other method is based on the evaluation of the local texture at pixel-level resolution while reducing the effects of variations in lighting. Fusing their approach realize robust object detection under varying illumination. Several experiments show the effectiveness of our approach.

Keywords

Object detection Adaptive background model Illumination change Parzen density estimation Radial Reach Filter 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tatsuya Tanaka
    • 1
  • Atsushi Shimada
    • 1
  • Daisaku Arita
    • 1
    • 2
  • Rin-ichiro Taniguchi
    • 1
  1. 1.Department of Intelligent SystemsKyushu UniversityJapan
  2. 2.Information Technologies and NanotechnologiesInstitute of SystemsJapan

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